code: def setup_method(self):
      super().setup_method()
      # [0, 1, 1, 1, 3, 1]
      self.predictions = torch.tensor(
          [
              [0.35, 0.25, 0.1, 0.1, 0.2],
              [0.1, 0.6, 0.1, 0.2, 0.0],
              [0.1, 0.6, 0.1, 0.2, 0.0],
              [0.1, 0.5, 0.1, 0.2, 0.0],
              [0.1, 0.2, 0.1, 0.7, 0.0],
              [0.1, 0.6, 0.1, 0.2, 0.0],
          ]
      )
      self.targets = torch.tensor([0, 4, 1, 0, 3, 0])
  
      # detailed target state
      self.pred_sum = [1, 4, 0, 1, 0]
      self.true_sum = [3, 1, 0, 1, 1]
      self.true_positive_sum = [1, 1, 0, 1, 0]
      self.true_negative_sum = [3, 2, 6, 5, 5]
      self.total_sum = [6, 6, 6, 6, 6]
  
      desired_precisions = [1.00, 0.25, 0.00, 1.00, 0.00]
      desired_recalls = [1 / 3, 1.00, 0.00, 1.00, 0.00]
      desired_fscores = [
          (2 * p * r) / (p + r) if p + r != 0.0 else 0.0
          for p, r in zip(desired_precisions, desired_recalls)
      ]
      self.desired_precisions = desired_precisions
      self.desired_recalls = desired_recalls
      self.desired_fscores = desired_fscores
  
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from sklearn.metrics import precision_recall_fscore_support
  from torch.testing import assert_allclose
  import pytest
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, run_distributed_test, global_distributed_metric
  from allennlp.training.metrics import FBetaMeasure
label_desc: Use image node_id 1 for calling the FBetaMeasureTest obj's underlying member method code with example usage: obj.setup_method() without return types